Machine Learning for High-Risk Applications: Techniques for Responsible AI


Machine Learning for High-Risk Applications: Approaches to Responsible AI 1st Edition
by Patrick Hall(Author), James Curtis(Author), Parul Pandey(Author)
Publisher finelybook 出版社: O’Reilly Media; 1st edition (May 23, 2023)
Language 语言: English
Print Length 页数: 466 pages
ISBN-10: 1098102436
ISBN-13: 9781098102432


Book Description
By finelybook

The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML’s true benefit, practitioners must understand how to mitigate its risks.
This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.
Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security
Learn how to create a successful and impactful AI risk management practice
Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework
Engage with interactive resources on GitHub and Colab

相关文件下载地址

下载地址 Download解决验证以访问链接!
打赏
未经允许不得转载:finelybook » Machine Learning for High-Risk Applications: Techniques for Responsible AI

评论 抢沙发

觉得文章有用就打赏一下

您的打赏,我们将继续给力更多优质内容

支付宝扫一扫

微信扫一扫